import numpy as np
from sklearn.linear_model import LogisticRegression

x=np.array([[2,3],[3,4],[6,5],[4,4],[3,2],[4,7],[5,4],[4,3],[7,5],[3,3],[4,4],[5,2]])
y=np.array([[1],[1],[1],[1],[1],[1][0],[0],[0],[0],[0],[0]])

x_test=np.array([[2,3],[3,4],[6,5],[4,4],[3,2],[4,7],[5,4],[4,3],[7,5],[3,3],[4,4],[5,2]])
y_test=np.array([[1],[1],[1],[1],[1],[1][0],[0],[0],[0],[0],[0]])

model = LogisticRegression
model.fit(x,y.ravel())

print(f"w={model.coef_},b={model.intercept_}")

r2 = modle.score(x_test,y_test)
print(f" {r2}")

y_pre=model.predict([[3,7]])
print(f"{y_pre}")